In my app multiple callbacks depend on a shared result. Calculating this shared result once is slow enough, but having multiple processes (or threads) repeatedly doing the calculation is even slower. I think I am looking for a cache-like solution that when the cache returns a miss, the requesting c…

I was able to solve this using a combination of: multiprocessing.lock() (instead of threading.lock()) gunicorn preloading If you use the preload option with gunicorn then you can share objects between processes using the multiprocessing family of proxy objects.

I’ve spun my code off into a module, which is available via pip https://github.com/sjtrny/jitcache I hope that others get some use out of this. I have an example using Dash here https://jitcache.readthedocs.io/en/latest/dash.html , which I have copied below: REDACTED: CODE OUTDATED. Refer to the fo…

Since the other day I have changed the design of jitcache to be more in line with LRU Cache and Flask-Caching by using a decorator instead. from jitcache import Cache cache = Cache() @cache.memoize def slow_fn(input_1, input_2, input_3=10): return input_1 * input_2 * input_3 print(slow_fn(10…

This looks really great. Once we investigate this a little bit more, we’ll consider adding it to the docs. Thanks for sharing this and keep the thread updated with your progress!

I like jitcache, I’ve tried it and it works well. Cool build. If you want an option that does this explicitly and feels like using a dictionary, I made https://github.com/russellromney/brain-plasma for this very purpose. Also available through pip. from brain_plasma import Brain brain = Brain() …

@sjtrny - I’m curious on how your @cache.memoize function works with .lock and concurrent requests. Let’s imagine that a callback def update takes 5 seconds to run and two requests are made, the second request 2 seconds after the first one. What happens? Does the memoize decorator put some kind of…

Yes that’s correct - the second request will only take 3 seconds.

Very nice. @russellthehippo - Does your plasma integration work the same way or does the end user need to program in the “waits”?

@chriddyp sadly brain-plasma has no locking control for the brain for the same object…which I suppose means brain-plasma doesn’t solve the key problem of doing the same thing multiple times in separate callbacks. Some behavior notes for brain-plasma: If the same client is trying to access an obje…

Preventing Wasteful Parallel Callbacks When Running Multiple Processes and Caching

Dash Python

chriddyp February 8, 2019, 2:06pm 4

Nice! Thanks for sharing!

Prevent dcc.Interval rerun until callback finishes

Topic		Replies	Views
Example 3 - Caching and Signalling from Sharing Data Between Callbacks page does not work as written Dash Python	1	452	September 21, 2021
Avoid running callback if inputs are the same Dash Python	9	5893	November 29, 2017
Could Dash execute what's inside a callback in parallel if there are several functions' calls inside? Dash Python	5	1949	October 10, 2019
Celery process terminated when same callback triggered multiple times Dash Python question	1	180	June 10, 2025
Multiple Callbacks for single inputs - not all are firing Dash Python	7	6089	June 1, 2021

Preventing Wasteful Parallel Callbacks When Running Multiple Processes and Caching

Related topics